Archive for April, 2018

A recent Contactpoint blog post described the way in which artificial intelligence and machine learning are impacting our world at large and how it works. This blog attempts to answer the question “How can business, both small and large, utilise AI to make significant advancements?” AI is certainly not a technology only available for large corporations.

I assert that there are 3 main ways that your business can benefit from artificial intelligence (‘AI’):

By integrating your website / app with software that has been improved by the use of AI. Such integration will significantly improve the value provided by your solution.

By using software that has been improved by AI for running your business, thus significantly improving the manner in which you run your business, on an ongoing basis.

By running your own deep learning exercises to determine the answer to a difficult question, which either improves your business performance or your understanding of your clients.

I expect that you already, perhaps unknowingly, use the outcome of AI or machine learning every day. Understanding it will help you harness it even more, so let’s explore just a few examples of each of these opportunities.

Integration
The Google search engine is underpinned by the use of AI – the more web pages it crawls, the better and better it gets at providing people with valid and useful search results. That’s part of the benefit of AI; traditional programming requires modification over time in response to the way people use it, with AI driven solutions, they learn and improve on their own.

Baidu, the so-called Chinese version of Google, allows you to upload an image, and request “similar images”. The search for similar images is not based on text around the images on a web page but solely uses the content of the images (2). Images, in technology terms, are made up of pixels of colour, which individually tell you very little. It is the manner in which the colours are combined, and the hard and soft edges around groups of pixels, which determine what is actually represented. AI underpins Baidu’s ability to find similar images – a very complex problem, and probably not something you could program a computer to perform. Traditionally the ability for a programmer to tell a computer how to achieve a goal was a prerequisite to solving that goal programmatically. With the use of AI, instead of telling the computer how to solve the problem, the program is allowed to train itself to solve the problem, getting better and better at achieving a task the more times it is performed.

We all use text search to find the things we need in Google or other search engines. It’s been possible to integrate the Google Search Engine into your website or app for many years, including restricting the search results to a particular domain or set of domains, thus providing excellent search results to your visitors without needing to write a search engine algorithm yourself. The ability to also search by images may be the differentiator that your website or app needs to deepen the value for your customers.

Other AI enriched applications that may enhance your application include:

Voice recognition – for speech to text and voice control of your app.

Language translation.

Image recognition e.g. Facebook suggesting name tags for people in photos you upload.

Route planning e.g. navigating from one place to another, taking traffic and other factors into consideration.

Clickup.com, a project management software, provides another example of integration. They announced this month that Clickup is now integrated with Alexa and Google Talk, allowing users to quickly interact with the online software by voice (3).
Google and Microsoft allow you to play with some of their AI enhanced functions via websites (4).

Operations
There are many functions that all businesses carry out. These functions are attracting the application of AI in order to make the tools used to complete these tasks, exponentially better than they have been before, and thereby attract new business.

Keeping up with the last news in your industry during your morning commute is now so much easier thanks to tools such as Voice Aloud which enables your smart phone to read an article to you while you drive (carefully of course). Your smart phone will also allow you to search using voice commands, using Google Voice Assistant or the iPhone Siri, allowing you to search hands free.
I recently asked my Android phone “Okay Google, what do I have on today?” expecting to have a list of my appointments read to me – it did that and, then started playing me 2 – 3 minute snippets of daily news recorded by various news agencies around Australia. It was a fantastic way to keep up-to-date and it “learned” that behaviour all on its own.

Google Search enables you to find relevant information, and this search is very accurate, powered by AI. It’s very important for Google’s revenue from online advertisements that Google Adwords provides relevant ads to searchers, because it is the relevance that inspires people to click on an ad, thus earning Google revenue. Similarly ads which appear in amongst Facebook news feeds are very reliable for showing your ad to the right audience, and once you have achieved excellent click through the result of Facebook’s AI research ensures that it will promote your ad to “look-a-like” audiences, based on what it knows about the people who already clicked. You can now much more confidently spend money on pay-per-click, because you can tailor your ads to specifically targeted audiences.

In a recent Contactpoint blog we talked about chatbots – the best of these are underpinned by AI, improving their results the more they are used so that they can help answer an inbound question before the human gets involved.

A number of online customer service and customer relationship management tools are now underpinned by AI. In these functions AI is bringing valuable insights as you use the tools, such as:
– Which clients are at the greatest risk of leaving you? (5)
– Which phrases and styles of interacting with customers produce the greatest sales results?
– What are the most important additional products or services to provide to your customers?

The better banking and financial management tools are now underpinned by AI to help you identify fraud (6). Similarly computer networks are being better secured from intrusion, viruses and malware now by solutions that use AI to detect unusual behaviour (7).

If your operations involve designing products and engineering, AI is making great inroads into design tools to help speed up the process (12).

Actionable Insights / Solving Problems
So far we have considered AI lead improvements to more general problems. Your business will be operating in a particular domain in which you are an expert, and in which there are very specific problems that have not yet been solved, or can’t be solved quickly & reliably for a large number of customers. This is where the power of AI may be the most potent, because machine learning / deep learning can be used to arrive at breakthroughs in your particular domain. Whilst it helps if you have lots of data in order to feed the deep learning process, for smaller businesses, you may be able to access public data to achieve the same goal, or use pre-existing neural networks to solve your similar problem.

Tools such as Chorus.ai are ready to take your organisation’s live data, in order to provide you with valuable insights in a specific operational area (8). In the case of Chorus.ai it analyses your meetings, particularly sales meetings, to help you get the best performance out of future meetings.

AI is being used to great effect by large corporations such as Walmart to quickly respond when high turnover products look like running out of stock, recently reporting a year-on-year 63% increase in sales (10).

Smaller organisations are also using AI to gain actionable insights, including a Zoo which now has a much better accuracy in predicting high attendance, and therefore staffing requirements, based on using AI to determine all the factors (not just weather) that increase visitor rates (10).

Domo is a tool created to help businesses, small and large, collate data from a wide range of sources (social media, ecommerce, chat bots etc), and help an organisation spot trends in real time (11).
In the area of product design and engineering, a concept called Generative Design underpinned by AI, is enabling faster design and many more possible designs to choose from by allowing all the constraints of the product to be entered, and then allowing the program to generate a large number of possible solutions (13).

However, for a problem more specific to your industry or expertise, you may need to perform a highly customised deep learning experiment. Once you have determined the question you need to answer for your specific area of expertise and industry, there are 7 steps in performing your own AI or deep learning experiment:

Gathering data

Preparing the data

Choosing an AI model to suit your question / domain

Training the model with data which contains the results / answers

Evaluation of the performance of the model

Tuning of the factors determined by the model

Applying the model to fresh data in order to gain insights or greater performance. (1)

As a business owner or leader you should be considering the way in which artificial intelligence or machine learning can change the way in which you operate and solve your customer’s problems. Don’t hesitate to get in contact if you would like to discuss how AI can be put to work for your organisation.

The goal of achieving artificial intelligence – a computer that can learn and respond like a human – began in the 1950s(1). However it is only in the last few years that we have seen great leaps forward towards this goal. The reason for the sudden improvements is attributed to break through in an area of technology called neural networks – programming that attempts to mimic the way the brain works, and a feature of the area of machine learning.

Up until the use of neural networks and machine learning, the act of programming a computer to perform a particular task – think displaying words on a screen, adding up columns of numbers, changing an image from colour to black and white – has required that a programmer can describe in exact detail the process of achieving that task. The human brain performs many tasks, seemingly effortlessly, that are virtually impossible for anyone to describe how they are done, beyond some vague concepts and pointers in the right direction. That’s not sufficient to be able to program a machine to do the task. Consider the task of identifying one human face from another – can you describe how your loved one looks, sufficient that another person who has never met them could pick them out in a crowd with any certainty? Very difficult! This is just one example of how amazing the human brain is when it comes to rapidly processing large amounts of information. We perform many such complex tasks almost simultaneously, without even realising.

A neural network is a programmatic attempt to replicate the manner in which it is believed the brain performs complex tasks. The diagram below is a typical representation of a neural network used to carry out a particular task. As an example, consider an input being an image of a face of a person who just passed the camera, and the task to be performed by the neural network being determining whether the image is “Joe Citizen”. The first round of analysis processes the input (camera image of a face) and then passes information about that image in the form of weightings down to the next level of processing. The second level receives that analysis, performs further analysis, and then passes another set of weightings down to the next level, and so on until the end result, which is the most likely answer to the question posed at the outset (where the attributes of Joe Citizen is already known by the program)? The “hidden layers” may comprise many different layers to allow deeper and deeper analysis and greater refinement aimed towards arriving at the correct answer.

Machine learning involves allowing a computer program to learn by working through a large amount of data, which also contains the answer to a particular question e.g. data on the observations of humans who both did and did not contract a particular disease in the future. The machine learning program will build a neural network of weightings required to answer the question being posed. Then that neural network is put to work against fresh data to further refine the learning, including humans providing feedback on the program’s accuracy. Finally, armed with all that learning stored in a neural network, the program can then be applied to new, live data in order to interpret that data … it turns out, with great speed and accuracy, surpassing that of humans (1).

The above is a very simplistic description of the way neural networks operate; computer scientists involved in the use of neural networks are constantly improving their performance. Neural networks are still in relatively early days of development, and already there are many different neural network models to choose from, some better at particular problem types compared to others.

An important distinguisher in neural networks compared to “regular” programming is that the neural network can be relatively easily tuned to perform better over time, as well as “learning” from more and more data. A “regular” computer program needs to be manually reprogrammed as requirements change, again requiring someone to describe exactly what is required, and understand all the implications of that change throughout the system.

Machine learning has been applied in the last few years, with great affect, in the following areas:

Image / Facial recognition – ever thought about how the image search feature of Google Images, or the speedy face tag suggestions by Facebook upon upload of a photo, have become so good? A person wanted for an alleged crime in China was picked up by security cameras in about 10 minutes of the wanted person entering a concert earlier this month (3).

Navigation & self-driving cars – being able to respond to incoming information, such as what other road users are doing around you, is essential for solving the problem of self-driving cars. The amount of technology involved in an autonomous car is awesome – and it needs to be given the life and death involved. “Even if it will take some time for fully autonomous vehicles to hit the market, AI is already transforming the inside of a car.” It is predicted that AI will first bring to our cars a host of so called “guardian-angel” type features to reduce the likelihood of accidents (11).

Speech recognition – in the last few years speech recognition (at least for native English speakers) has become very accurate, requiring very little training for a particular person. I now control my mobile phone using voice on a regular basis, because talking to my phone is much faster than typing – apparently 3 times faster according to a study by Standford University (4). Google’s latest speech-to-text system, called Tacotron 2, will add inflection to words based on punctuation to further improve understanding (5) and making it even more human-like when it is reading text to you, or responding with an answer to a question. Speech recognition in devices such as Google Home and Amazon Alexa are making simple tasks much easier. The article entitled “Amazon Echo has transformed the way I live in my apartment – here are my 19 favourite features” shows how speech recognition is being used for hands-free computer assistance in a simple home context (9). Applications of this technology are vast and life-changing for those who don’t have free hands (e.g. a surgeon at work) or are not able to type.

Prediction – more quickly and accurately diagnosing a current situation or predicting that a current set of information is an indicator of a future state e.g. in diagnosing disease, predicting financial market movements, identifying criminal behaviour such as insurance or banking fraud (13). The ability of a neural network to process vast amounts of data quickly, and build its own conclusions with regard to the impact of one factor on another (learn) is already helping doctors to more accurately diagnose conditions such as heart disease (12). Reducing the acceptable level of inaccuracy in medical diagnosis will lead to much better patient outcomes and reduce the cost of healthcare to our ageing population.

Playing games – a lot of AI research uses games to work out how to train a computer to learn. (8) From time to time I play an online version of the Settlers of Catan board game; when players leave the game (ostensibly because they have lost internet connection … usually it’s when they are losing!), you get the option to continue to game and have AI finish it on their behalf. It amuses me that I find myself, and others, immediately ganging up on the AI player. I mean, they won’t care if you make their game difficult – they’re a robot after all! It was actually the success of a computer to beat the best human players of the hardest game we play that heralded the success of artificial intelligence, and made the world take notice of its capabilities (14). “In the course of winning, [the robot] somehow taught the world completely new knowledge about perhaps the most studied and contemplated game in history.”

But, will the rise of artificial intelligence take away our jobs? Some say yes, others say no (6), but they all say that the new jobs created due to artificial intelligence will be different to current roles, and require different skills (7).

Worse than job loss, will AI cause a computer vs human war or lead to our extinction? Elon Musk is well known for his warnings against AI. It could be viewed that the pressure he has applied to the technology industry helped to lead to an agreement that the technology giants will only use AI for good (10).

I don’t believe that AI will ever result in a computer takeover of the world, because there is more that makes humans different from other animals … not just our ability to think. Reproducing just our ability to think, learn and make decisions, even in a super-human way, does not make a computer human. The capacity for machine learning / deep learning to significantly improve our lives, particularly in the areas of health and solving some of our most challenging problems, is exciting. However, I believe that it is right to be cautious; to move ahead with the knowledge that machine learning could also be used for harmful purposes. Computers can also “learn” the negative elements of humanity (15).

Business owners, innovators and leaders should consider how machine learning might be harnessed for your organisation in order to provide better value, predict more accurately, respond more quickly, or make break-throughs in knowledge in your problem domain. Let’s harness artificial intelligence for good! Read more about “How Business (big and small) can Harness Artificial Intelligence“.